Esempio n. 1
0
    def process(self, args, outputs):
        tree = outputs['tree']
        reconstruction = outputs['reconstruction']
        verbose = '-verbose' if args.verbose else ''

        # define paths and create working directories
        system.mkdir_p(tree.odm_orthophoto)

        if not io.file_exists(tree.odm_orthophoto_file) or self.rerun():

            # odm_orthophoto definitions
            kwargs = {
                'bin': context.odm_modules_path,
                'log': tree.odm_orthophoto_log,
                'ortho': tree.odm_orthophoto_file,
                'corners': tree.odm_orthophoto_corners,
                'res': 1.0 / (gsd.cap_resolution(args.orthophoto_resolution, tree.opensfm_reconstruction, ignore_gsd=args.ignore_gsd) / 100.0),
                'verbose': verbose
            }

            # Have geo coordinates?
            georef = reconstruction.georef

            # Check if the georef object is initialized
            # (during a --rerun this might not be)
            # TODO: this should be moved to a more central location?
            if reconstruction.is_georeferenced() and not reconstruction.georef.valid_utm_offsets():
                georeferencing_dir = tree.odm_georeferencing if args.use_3dmesh and not args.skip_3dmodel else tree.odm_25dgeoreferencing
                odm_georeferencing_model_txt_geo_file = os.path.join(georeferencing_dir, tree.odm_georeferencing_model_txt_geo)

                if io.file_exists(odm_georeferencing_model_txt_geo_file):
                    reconstruction.georef.extract_offsets(odm_georeferencing_model_txt_geo_file)
                else:
                    log.ODM_WARNING('Cannot read UTM offset from {}. An orthophoto will not be generated.'.format(odm_georeferencing_model_txt_geo_file))

            if reconstruction.is_georeferenced():
                if args.use_3dmesh:
                    kwargs['model_geo'] = os.path.join(tree.odm_texturing, tree.odm_georeferencing_model_obj_geo)
                else:
                    kwargs['model_geo'] = os.path.join(tree.odm_25dtexturing, tree.odm_georeferencing_model_obj_geo)
            else:
                if args.use_3dmesh:
                    kwargs['model_geo'] = os.path.join(tree.odm_texturing, tree.odm_textured_model_obj)
                else:
                    kwargs['model_geo'] = os.path.join(tree.odm_25dtexturing, tree.odm_textured_model_obj)

            # run odm_orthophoto
            system.run('{bin}/odm_orthophoto -inputFile {model_geo} '
                       '-logFile {log} -outputFile {ortho} -resolution {res} {verbose} '
                       '-outputCornerFile {corners}'.format(**kwargs))

            # Create georeferenced GeoTiff
            geotiffcreated = False

            if reconstruction.is_georeferenced() and reconstruction.georef.valid_utm_offsets():
                ulx = uly = lrx = lry = 0.0
                with open(tree.odm_orthophoto_corners) as f:
                    for lineNumber, line in enumerate(f):
                        if lineNumber == 0:
                            tokens = line.split(' ')
                            if len(tokens) == 4:
                                ulx = float(tokens[0]) + \
                                    float(reconstruction.georef.utm_east_offset)
                                lry = float(tokens[1]) + \
                                    float(reconstruction.georef.utm_north_offset)
                                lrx = float(tokens[2]) + \
                                    float(reconstruction.georef.utm_east_offset)
                                uly = float(tokens[3]) + \
                                    float(reconstruction.georef.utm_north_offset)
                log.ODM_INFO('Creating GeoTIFF')

                orthophoto_vars = orthophoto.get_orthophoto_vars(args)

                kwargs = {
                    'ulx': ulx,
                    'uly': uly,
                    'lrx': lrx,
                    'lry': lry,
                    'vars': ' '.join(['-co %s=%s' % (k, orthophoto_vars[k]) for k in orthophoto_vars]),
                    'proj': reconstruction.georef.proj4(),
                    'png': tree.odm_orthophoto_file,
                    'tiff': tree.odm_orthophoto_tif,
                    'log': tree.odm_orthophoto_tif_log,
                    'max_memory': get_max_memory(),
                }

                system.run('gdal_translate -a_ullr {ulx} {uly} {lrx} {lry} '
                           '{vars} '
                           '-a_srs \"{proj}\" '
                           '--config GDAL_CACHEMAX {max_memory}% '
                           '{png} {tiff} > {log}'.format(**kwargs))

                bounds_file_path = os.path.join(tree.odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')
                    
                # Cutline computation, before cropping
                # We want to use the full orthophoto, not the cropped one.
                if args.orthophoto_cutline:
                    compute_cutline(tree.odm_orthophoto_tif, 
                                    bounds_file_path,
                                    os.path.join(tree.odm_orthophoto, "cutline.gpkg"),
                                    args.max_concurrency,
                                    tmpdir=os.path.join(tree.odm_orthophoto, "grass_cutline_tmpdir"),
                                    scale=0.25)

                if args.crop > 0:
                    Cropper.crop(bounds_file_path, tree.odm_orthophoto_tif, orthophoto_vars)

                if args.build_overviews:
                    orthophoto.build_overviews(tree.odm_orthophoto_tif)

                geotiffcreated = True
            if not geotiffcreated:
                log.ODM_WARNING('No geo-referenced orthophoto created due '
                                'to missing geo-referencing or corner coordinates.')

        else:
            log.ODM_WARNING('Found a valid orthophoto in: %s' % tree.odm_orthophoto_file)
Esempio n. 2
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    def process(self, args, outputs):
        tree = outputs['tree']
        reconstruction = outputs['reconstruction']
        verbose = '-verbose' if args.verbose else ''

        # define paths and create working directories
        system.mkdir_p(tree.odm_orthophoto)

        if not io.file_exists(tree.odm_orthophoto_tif) or self.rerun():
            gsd_error_estimate = 0.1
            ignore_resolution = False
            if not reconstruction.is_georeferenced():
                # Match DEMs
                gsd_error_estimate = -3
                ignore_resolution = True

            resolution = 1.0 / (
                gsd.cap_resolution(args.orthophoto_resolution,
                                   tree.opensfm_reconstruction,
                                   gsd_error_estimate=gsd_error_estimate,
                                   ignore_gsd=args.ignore_gsd,
                                   ignore_resolution=ignore_resolution,
                                   has_gcp=reconstruction.has_gcp()) / 100.0)

            # odm_orthophoto definitions
            kwargs = {
                'bin': context.odm_modules_path,
                'log': tree.odm_orthophoto_log,
                'ortho': tree.odm_orthophoto_render,
                'corners': tree.odm_orthophoto_corners,
                'res': resolution,
                'bands': '',
                'verbose': verbose
            }

            models = []

            if args.use_3dmesh:
                base_dir = tree.odm_texturing
            else:
                base_dir = tree.odm_25dtexturing

            model_file = tree.odm_textured_model_obj

            if reconstruction.multi_camera:
                for band in reconstruction.multi_camera:
                    primary = band['name'] == get_primary_band_name(
                        reconstruction.multi_camera, args.primary_band)
                    subdir = ""
                    if not primary:
                        subdir = band['name'].lower()
                    models.append(os.path.join(base_dir, subdir, model_file))
                kwargs['bands'] = '-bands %s' % (','.join(
                    [quote(b['name']) for b in reconstruction.multi_camera]))
            else:
                models.append(os.path.join(base_dir, model_file))

            kwargs['models'] = ','.join(map(quote, models))

            # run odm_orthophoto
            system.run(
                '{bin}/odm_orthophoto -inputFiles {models} '
                '-logFile {log} -outputFile {ortho} -resolution {res} {verbose} '
                '-outputCornerFile {corners} {bands}'.format(**kwargs))

            # Create georeferenced GeoTiff
            geotiffcreated = False

            if reconstruction.is_georeferenced():
                ulx = uly = lrx = lry = 0.0
                with open(tree.odm_orthophoto_corners) as f:
                    for lineNumber, line in enumerate(f):
                        if lineNumber == 0:
                            tokens = line.split(' ')
                            if len(tokens) == 4:
                                ulx = float(tokens[0]) + \
                                      float(reconstruction.georef.utm_east_offset)
                                lry = float(tokens[1]) + \
                                      float(reconstruction.georef.utm_north_offset)
                                lrx = float(tokens[2]) + \
                                      float(reconstruction.georef.utm_east_offset)
                                uly = float(tokens[3]) + \
                                      float(reconstruction.georef.utm_north_offset)
                log.ODM_INFO('Creating GeoTIFF')

                orthophoto_vars = orthophoto.get_orthophoto_vars(args)

                kwargs = {
                    'ulx':
                    ulx,
                    'uly':
                    uly,
                    'lrx':
                    lrx,
                    'lry':
                    lry,
                    'vars':
                    ' '.join([
                        '-co %s=%s' % (k, orthophoto_vars[k])
                        for k in orthophoto_vars
                    ]),
                    'proj':
                    reconstruction.georef.proj4(),
                    'input':
                    tree.odm_orthophoto_render,
                    'output':
                    tree.odm_orthophoto_tif,
                    'log':
                    tree.odm_orthophoto_tif_log,
                    'max_memory':
                    get_max_memory(),
                }

                system.run('gdal_translate -a_ullr {ulx} {uly} {lrx} {lry} '
                           '{vars} '
                           '-a_srs \"{proj}\" '
                           '--config GDAL_CACHEMAX {max_memory}% '
                           '--config GDAL_TIFF_INTERNAL_MASK YES '
                           '{input} {output} > {log}'.format(**kwargs))

                bounds_file_path = os.path.join(
                    tree.odm_georeferencing,
                    'odm_georeferenced_model.bounds.gpkg')

                # Cutline computation, before cropping
                # We want to use the full orthophoto, not the cropped one.
                if args.orthophoto_cutline:
                    cutline_file = os.path.join(tree.odm_orthophoto,
                                                "cutline.gpkg")

                    compute_cutline(tree.odm_orthophoto_tif,
                                    bounds_file_path,
                                    cutline_file,
                                    args.max_concurrency,
                                    tmpdir=os.path.join(
                                        tree.odm_orthophoto,
                                        "grass_cutline_tmpdir"),
                                    scale=0.25)

                    orthophoto.compute_mask_raster(
                        tree.odm_orthophoto_tif,
                        cutline_file,
                        os.path.join(tree.odm_orthophoto,
                                     "odm_orthophoto_cut.tif"),
                        blend_distance=20,
                        only_max_coords_feature=True)

                orthophoto.post_orthophoto_steps(args, bounds_file_path,
                                                 tree.odm_orthophoto_tif,
                                                 tree.orthophoto_tiles)

                # Generate feathered orthophoto also
                if args.orthophoto_cutline:
                    orthophoto.feather_raster(
                        tree.odm_orthophoto_tif,
                        os.path.join(tree.odm_orthophoto,
                                     "odm_orthophoto_feathered.tif"),
                        blend_distance=20)

                geotiffcreated = True
            if not geotiffcreated:
                if io.file_exists(tree.odm_orthophoto_render):
                    pseudogeo.add_pseudo_georeferencing(
                        tree.odm_orthophoto_render)
                    log.ODM_INFO(
                        "Renaming %s --> %s" %
                        (tree.odm_orthophoto_render, tree.odm_orthophoto_tif))
                    os.rename(tree.odm_orthophoto_render,
                              tree.odm_orthophoto_tif)
                else:
                    log.ODM_WARNING(
                        "Could not generate an orthophoto (it did not render)")
        else:
            log.ODM_WARNING('Found a valid orthophoto in: %s' %
                            tree.odm_orthophoto_tif)

        if args.optimize_disk_space and io.file_exists(
                tree.odm_orthophoto_render):
            os.remove(tree.odm_orthophoto_render)
Esempio n. 3
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def process(args, current_path, max_concurrency, reconstruction):
    #args = vars(args)
    orthophoto_cutline = True
    odm_orthophoto = io.join_paths(current_path, 'orthophoto')
    odm_orthophoto_path = odm_orthophoto
    odm_orthophoto_render = io.join_paths(odm_orthophoto_path,
                                          'odm_orthophoto_render.tif')
    odm_orthophoto_tif = io.join_paths(odm_orthophoto_path,
                                       'odm_orthophoto.tif')
    odm_orthophoto_corners = io.join_paths(odm_orthophoto_path,
                                           'odm_orthophoto_corners.tif')
    odm_orthophoto_log = io.join_paths(odm_orthophoto_path,
                                       'odm_orthophoto_log.tif')
    odm_orthophoto_tif_log = io.join_paths(odm_orthophoto_path,
                                           'gdal_translate_log.txt')
    odm_25dgeoreferencing = io.join_paths(current_path, 'odm_georeferencing')
    odm_georeferencing = io.join_paths(current_path, 'odm_georeferencing')

    odm_georeferencing_coords = io.join_paths(odm_georeferencing, 'coords.txt')
    odm_georeferencing_gcp = io.find('gcp_list.txt', current_path)
    odm_georeferencing_gcp_utm = io.join_paths(odm_georeferencing,
                                               'gcp_list_utm.txt')
    odm_georeferencing_utm_log = io.join_paths(
        odm_georeferencing, 'odm_georeferencing_utm_log.txt')
    odm_georeferencing_log = 'odm_georeferencing_log.txt'
    odm_georeferencing_transform_file = 'odm_georeferencing_transform.txt'
    odm_georeferencing_proj = 'proj.txt'
    odm_georeferencing_model_txt_geo = 'odm_georeferencing_model_geo.txt'
    odm_georeferencing_model_obj_geo = 'odm_textured_model_geo.obj'
    odm_georeferencing_xyz_file = io.join_paths(odm_georeferencing,
                                                'odm_georeferenced_model.csv')
    odm_georeferencing_las_json = io.join_paths(odm_georeferencing, 'las.json')
    odm_georeferencing_model_laz = io.join_paths(
        odm_georeferencing, 'odm_georeferenced_model.laz')
    odm_georeferencing_model_las = io.join_paths(
        odm_georeferencing, 'odm_georeferenced_model.las')
    odm_georeferencing_dem = io.join_paths(odm_georeferencing,
                                           'odm_georeferencing_model_dem.tif')

    opensfm_reconstruction = io.join_paths(current_path, 'reconstruction.json')
    odm_texturing = io.join_paths(current_path, 'mvs')
    odm_textured_model_obj = io.join_paths(odm_texturing,
                                           'odm_textured_model.obj')
    images_dir = io.join_paths(current_path, 'images')

    reconstruction = reconstruction

    verbose = ''
    #"-verbose"

    # define paths and create working directories
    system.mkdir_p(odm_orthophoto)

    if not io.file_exists(odm_orthophoto_tif):
        gsd_error_estimate = 0.1
        ignore_resolution = False
        if not reconstruction.is_georeferenced():
            # Match DEMs
            gsd_error_estimate = -3
            ignore_resolution = True
        orthophoto_resolution = 5
        resolution = 1.0 / (
            gsd.cap_resolution(orthophoto_resolution,
                               opensfm_reconstruction,
                               gsd_error_estimate=gsd_error_estimate,
                               ignore_gsd=True,
                               ignore_resolution=ignore_resolution,
                               has_gcp=reconstruction.has_gcp()) / 100.0)

        # odm_orthophoto definitions
        kwargs = {
            'bin': context.odm_modules_path,
            'log': odm_orthophoto_log,
            'ortho': odm_orthophoto_render,
            'corners': odm_orthophoto_corners,
            'res': resolution,
            'bands': '',
            'verbose': verbose
        }

        # Check if the georef object is initialized
        # (during a --rerun this might not be)
        # TODO: this should be moved to a more central location?
        if reconstruction.is_georeferenced(
        ) and not reconstruction.georef.valid_utm_offsets():
            georeferencing_dir = odm_georeferencing  #if args.use_3dmesh and not args.skip_3dmodel else odm_25dgeoreferencing
            odm_georeferencing_model_txt_geo_file = os.path.join(
                georeferencing_dir, odm_georeferencing_model_txt_geo)

            if io.file_exists(odm_georeferencing_model_txt_geo_file):
                reconstruction.georef.extract_offsets(
                    odm_georeferencing_model_txt_geo_file)
            else:
                log.ODM_WARNING('Cannot read UTM offset from {}.'.format(
                    odm_georeferencing_model_txt_geo_file))

        models = []

        base_dir = odm_texturing

        if reconstruction.is_georeferenced():
            model_file = odm_georeferencing_model_obj_geo
        else:
            model_file = odm_textured_model_obj

        if reconstruction.multi_camera:
            for band in reconstruction.multi_camera:
                primary = band == reconstruction.multi_camera[0]
                subdir = ""
                if not primary:
                    subdir = band['name'].lower()
                models.append(os.path.join(base_dir, subdir, model_file))
            kwargs['bands'] = '-bands %s' % (','.join([
                quote(b['name'].lower()) for b in reconstruction.multi_camera
            ]))
        else:
            models.append(os.path.join(base_dir, model_file))

        kwargs['models'] = ','.join(map(quote, models))

        # run odm_orthophoto
        system.run(
            '{bin}/odm_orthophoto -inputFiles {models} '
            '-logFile {log} -outputFile {ortho} -resolution {res} {verbose} '
            '-outputCornerFile {corners} {bands}'.format(**kwargs))

        # Create georeferenced GeoTiff
        geotiffcreated = False

        if reconstruction.is_georeferenced(
        ) and reconstruction.georef.valid_utm_offsets():
            ulx = uly = lrx = lry = 0.0
            with open(odm_orthophoto_corners) as f:
                for lineNumber, line in enumerate(f):
                    if lineNumber == 0:
                        tokens = line.split(' ')
                        if len(tokens) == 4:
                            ulx = float(tokens[0]) + \
                                float(reconstruction.georef.utm_east_offset)
                            lry = float(tokens[1]) + \
                                float(reconstruction.georef.utm_north_offset)
                            lrx = float(tokens[2]) + \
                                float(reconstruction.georef.utm_east_offset)
                            uly = float(tokens[3]) + \
                                float(reconstruction.georef.utm_north_offset)
            log.ODM_INFO('Creating GeoTIFF')

            orthophoto_vars = orthophoto.get_orthophoto_vars(args)

            kwargs = {
                'ulx':
                ulx,
                'uly':
                uly,
                'lrx':
                lrx,
                'lry':
                lry,
                'vars':
                ' '.join([
                    '-co %s=%s' % (k, orthophoto_vars[k])
                    for k in orthophoto_vars
                ]),
                'proj':
                reconstruction.georef.proj4(),
                'input':
                odm_orthophoto_render,
                'output':
                odm_orthophoto_tif,
                'log':
                odm_orthophoto_tif_log,
                'max_memory':
                get_max_memory(),
            }

            system.run('gdal_translate -a_ullr {ulx} {uly} {lrx} {lry} '
                       '{vars} '
                       '-a_srs \"{proj}\" '
                       '--config GDAL_CACHEMAX {max_memory}% '
                       '--config GDAL_TIFF_INTERNAL_MASK YES '
                       '{input} {output} > {log}'.format(**kwargs))

            bounds_file_path = os.path.join(
                odm_georeferencing, 'odm_georeferenced_model.bounds.gpkg')

            # Cutline computation, before cropping
            # We want to use the full orthophoto, not the cropped one.
            pio = True
            if pio:
                cutline_file = os.path.join(odm_orthophoto, "cutline.gpkg")

                compute_cutline(odm_orthophoto_tif,
                                bounds_file_path,
                                cutline_file,
                                max_concurrency,
                                tmpdir=os.path.join(odm_orthophoto,
                                                    "grass_cutline_tmpdir"),
                                scale=0.25)

                orthophoto.compute_mask_raster(odm_orthophoto_tif,
                                               cutline_file,
                                               os.path.join(
                                                   odm_orthophoto,
                                                   "odm_orthophoto_cut.tif"),
                                               blend_distance=20,
                                               only_max_coords_feature=True)

            orthophoto.post_orthophoto_steps(args, bounds_file_path,
                                             odm_orthophoto_tif)

            # Generate feathered orthophoto also
            if pio:
                orthophoto.feather_raster(odm_orthophoto_tif,
                                          os.path.join(
                                              odm_orthophoto,
                                              "odm_orthophoto_feathered.tif"),
                                          blend_distance=20)

            geotiffcreated = True
        if not geotiffcreated:
            if io.file_exists(odm_orthophoto_render):
                pseudogeo.add_pseudo_georeferencing(odm_orthophoto_render)
                log.ODM_INFO("Renaming %s --> %s" %
                             (odm_orthophoto_render, odm_orthophoto_tif))
                os.rename(odm_orthophoto_render, odm_orthophoto_tif)
            else:
                log.ODM_WARNING(
                    "Could not generate an orthophoto (it did not render)")
    else:
        log.ODM_WARNING('Found a valid orthophoto in: %s' % odm_orthophoto_tif)

    #generate png

    orthophoto.generate_png(odm_orthophoto_tif)
Esempio n. 4
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    # Bounds (GPKG)
    run("ddb info --format geojson --geometry polygon \"%s\" > \"%s\"" %
        (f, geojson))
    run("ogr2ogr \"%s\" \"%s\"" % (gpkg, geojson))

log.ODM_INFO("Computing cutlines")

projected_images = glob.glob(os.path.join(tmp_path, "*.tif"))
all_orthos_and_ortho_cuts = []

for f in projected_images:
    name, _ = os.path.splitext(os.path.basename(f))
    cutline_file = os.path.join(tmp_path, "%s_cutline.gpkg" % name)
    bounds_file_path = os.path.join(tmp_path, "%s.gpkg" % name)

    compute_cutline(f, bounds_file_path, cutline_file, 4, scale=1)

    cut_raster = os.path.join(tmp_path, "%s_cut.tif" % name)
    orthophoto.compute_mask_raster(f,
                                   cutline_file,
                                   cut_raster,
                                   blend_distance=20,
                                   only_max_coords_feature=True)

    feathered_raster = os.path.join(tmp_path, "%s_feathered.tif" % name)

    orthophoto.feather_raster(f, feathered_raster, blend_distance=20)

    all_orthos_and_ortho_cuts.append([feathered_raster, cut_raster])

log.ODM_INFO("Merging...")